中文摘要：

以新疆喀纳斯自然保护区为研究区, 评价了HJ-CCD影像数据估算植被叶面积指数（LAI）的能力及其对大气订正方法的敏感性.分别利用6S和FLAASH两种大气订正模型对HJ1B-CCD2影像进行大气订正, 比较了大气订正前后不同植被（针叶林、阔叶林、针阔混交林和草地）反射率及5种植被指数（NDVI、SR、SAVI、MSR、ARVI）的变化, 进而建立了4种植被类型LAI的遥感估算模型, 分析了LAI的空间分布格局.结果表明： 大气订正后可见光波段的反射率降低, 6S模型订正后近红外波段的反射率上升, 而FLAASH模型订正后近红外波段的反射率下降.大气订正后NDVI、SR、SAVI（除针叶林）和MSR上升, 6S模型订正后所有植被类型的ARVI下降, FLAASH模型订正后针叶林和阔叶林的ARVI上升, 而针阔混交林和草地的ARVI下降.大气订正提高了植被指数与LAI之间的相关性, 对于针叶林、阔叶林、针阔混交林而言, 利用6S模型订正后的反射率建立的模型优于FLAASH模型订正后的反射率建立的模型, 而草地却相反.经过大气订正, HJ-CCD影像数据可应用于研究区植被LAI的估算.研究区LAI的高值集中在湖泊和河流附近, 低值分布在海拔较高处.山地森林草原带、亚高山森林带、高山灌丛草甸带、高山冻原、高山冰川带植被LAI的平均值分别为2.6、3.9、2.5、1.7和1.0.

英文摘要：

The ability of HJ-CCD remote sensing data to retrieve vegetation leaf area index （LAI） and its sensitivity to atmospheric correction methods were investigated in the Kanas National Nature Reserve, Xinjiang. The 6S and FLAASH models were employed to implement atmospheric corrections for the remote sensing data. The changes in the reflectance and vegetation indices （NDVI, SR, SAVI, MSR, ARVI） of different land cover types （needle leaved forests, broad leaved forests, mixed forests, and grasslands） with and without atmospheric correction were analyzed. Then, the best fitted models for estimating LAI were built with the field measurements of LAI and spatial distribution patterns of LAI were analyzed. The results show that after atmospheric correction, the reflectance in the visible bands decrease. Reflectance in the near infrared band increases with the atmospheric correction by the 6S model and decreases with the atmospheric correction implemented by the FLAASH model. Consequently, atmospheric correction causes NDVI, SR, MSR and SAVI （except needle leaved forests） to increase. As to ARVI, the influence of atmospheric correction is related to the correction model used and vegetation types. With the atmospheric correction by the 6S model, ARVI decreases for all land cover types. The atmospheric correction conducted by the FLAASH model causes ARVI to increase for need leaved and broad leaved forests and to decrease for mixed forests and grasslands. Atmospheric correction enhances the linkage between LAI and vegetation indices. The models based on reflectance with the atmospheric correction by the 6S model are better than the models base on reflectance with atmospheric correction by the FLAASH model for forests （needle leaved forests, broad leaved forests, and mixed forests）. As to retrieval of LAI for grasslands, the model based on reflectance with the atmospheric correction by the FLAASH model outperforms the model based on reflectance with the atmospheric correction by the 6S. The results indicate